In all of the above results, we used the ability to classify the sensitive attributes as a proxy for bias being contained within the embeddings.
While this is a standard approach, e.g., see [1]}, and an intuitive method for evaluating representational invariance—a natural question is whether the adversarial regularization also decreases bias in the edge prediction tasks.
Ideally, after filtering the embeddings, we would have that the edge predictions themselves are not biased according to the sensitive attributes.
